1,251 research outputs found
SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams
Learning from demonstration (LfD) and imitation learning offer new paradigms
for transferring task behavior to robots. A class of methods that enable such
online learning require the robot to observe the task being performed and
decompose the sensed streaming data into sequences of state-action pairs, which
are then input to the methods. Thus, recognizing the state-action pairs
correctly and quickly in sensed data is a crucial prerequisite for these
methods. We present SA-Net a deep neural network architecture that recognizes
state-action pairs from RGB-D data streams. SA-Net performed well in two
diverse robotic applications of LfD -- one involving mobile ground robots and
another involving a robotic manipulator -- which demonstrates that the
architecture generalizes well to differing contexts. Comprehensive evaluations
including deployment on a physical robot show that \sanet{} significantly
improves on the accuracy of the previous method that utilizes traditional image
processing and segmentation.Comment: (in press
Open-Ended Evolutionary Robotics: an Information Theoretic Approach
This paper is concerned with designing self-driven fitness functions for
Embedded Evolutionary Robotics. The proposed approach considers the entropy of
the sensori-motor stream generated by the robot controller. This entropy is
computed using unsupervised learning; its maximization, achieved by an on-board
evolutionary algorithm, implements a "curiosity instinct", favouring
controllers visiting many diverse sensori-motor states (sms). Further, the set
of sms discovered by an individual can be transmitted to its offspring, making
a cultural evolution mode possible. Cumulative entropy (computed from ancestors
and current individual visits to the sms) defines another self-driven fitness;
its optimization implements a "discovery instinct", as it favours controllers
visiting new or rare sensori-motor states. Empirical results on the benchmark
problems proposed by Lehman and Stanley (2008) comparatively demonstrate the
merits of the approach
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